The work in this proposal will develop and make available to researchers new tools for studying time-varying changes in event-related human electrophysiological signals, such as those that might occur due to learning, habituation, facilitation, adaptation, or pharmacological intervention. Conventional average event- related electrical potentials and magnetic fields permit non-invasive exploration of underlying brain processes with millisecond resolution, but do not allow the quantitative analysis of interactions between events in a straightforward way. The work described in this proposal will implement experimental task- related system identification (Volterra) methods that will permit us to describe quantitatively both the linear responses to events (which are related closely to conventional average waveforms) and the non-linear dynamics of event interactions (which are not addressed by conventional averaging). Algorithms will be developed, embodied in computer software, and evaluated using simulated and experimental data. The resulting software will form part of our EMSE Suite commercial software package. A fuller understanding of these time varying changes will extend our knowledge in basic cognitive neuroscience, as well as its clinical applications, such as schizophrenia and other disorders of brain dynamics. [unreadable] [unreadable] [unreadable]